{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(ch:pa_cleaning_pa)=\n",
    "# Wrangling PurpleAir Sensor Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous section, we analyzed data from AQS site `06-067-0010`.\n",
    "The matching PurpleAir sensor is named `AMTS_TESTINGA`, and we've used\n",
    "the PurpleAir website to download the data for this sensor into the _data/purpleair_AMTS_ folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 nolan  staff    50M Jan 25 16:35 data/purpleair_AMTS/AMTS_\n",
      "-rw-r--r--  1 nolan  staff    50M Jan 25 16:35 data/purpleair_AMTS/AMTS_\n",
      "-rw-r--r--  1 nolan  staff    48M Jan 25 16:35 data/purpleair_AMTS/AMTS_\n",
      "-rw-r--r--  1 nolan  staff    50M Jan 25 16:35 data/purpleair_AMTS/AMTS_\n"
     ]
    }
   ],
   "source": [
    "!ls -alh data/purpleair_AMTS/* | cut -c 1-72"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are four CSV files. Their names are quite long, and the beginning of each is identical.\n",
    "The data dictionary for the PurpleAir data says that each sensor\n",
    "has two separate instruments, A and B, that each record data.\n",
    "Note that the PurpleAir site we used to collect these data and the accompanying data dictionary has been downgraded. The data are now available through a REST API. The [site that documents the API](https://api.purpleair.com/#api-sensors-get-sensor-data) also contains information about the fields. The topic of REST is covered in {numref}`Chapter %s <ch:web>`.)\n",
    "Let's examine the later portions of the filenames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TESTING (outside) (38.568404 -121.493163) Primary Real Time 05_20_20\n",
      "TESTING (outside) (38.568404 -121.493163) Secondary Real Time 05_20_\n",
      "TESTING B (undefined) (38.568404 -121.493163) Primary Real Time 05_2\n",
      "TESTING B (undefined) (38.568404 -121.493163) Secondary Real Time 05\n"
     ]
    }
   ],
   "source": [
    "!ls -alh data/purpleair_AMTS/* | cut -c 73-140"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the first two CSV files correspond to instrument A\n",
    "and the last two to B.\n",
    "Having two instruments is useful for data cleaning; if A and B disagree about\n",
    "a measurement, we might question the integrity of the measurement and decide to\n",
    "remove it.\n",
    "\n",
    "The data dictionary also mentions that each instrument records Primary and\n",
    "Secondary data. The Primary data contains the fields we're interested in: PM2.5,\n",
    "temperature, and humidity. The Secondary data contains data for other particle\n",
    "sizes, like PM1.0 and PM10. So we work only with the Primary files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our tasks are similar to those of the previous section, with the addition of addressing readings from two instruments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by loading in the data. \n",
    "When CSV files have long names, we can assign the filenames into a Python variable\n",
    "to more easily load the files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('data/purpleair_AMTS/AMTS_TESTING (outside) (38.568404 -121.493163) Primary Real Time 05_20_2018 12_29_2019.csv')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "data_folder = Path('data/purpleair_AMTS')\n",
    "pa_csvs = sorted(data_folder.glob('*.csv'))\n",
    "pa_csvs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(672755, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_full = pd.read_csv(pa_csvs[0])\n",
    "pa_full.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the columns to see which ones we need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['created_at', 'entry_id', 'PM1.0_CF1_ug/m3', 'PM2.5_CF1_ug/m3',\n",
       "       'PM10.0_CF1_ug/m3', 'UptimeMinutes', 'RSSI_dbm', 'Temperature_F',\n",
       "       'Humidity_%', 'PM2.5_ATM_ug/m3', 'Unnamed: 10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_full.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we're interested in PM2.5, it appears there are two columns that\n",
    "contain PM2.5 data: `PM2.5_CF1_ug/m3` and `PM2.5_ATM_ug/m3`.\n",
    "We investigate the difference between these two columns to find\n",
    "that PurpleAir sensors use two different methods to convert a raw laser recording into a PM2.5 number.\n",
    "These two calculations correspond to the CF1 and ATM columns.\n",
    "Barkjohn found that using CF1 produced better results than ATM, so we keep that column, \n",
    "along with the date, temperature, and relative humidity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>PM25cf1</th>\n",
       "      <th>TempF</th>\n",
       "      <th>RH</th>\n",
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       "      <td>2018-05-20 00:00:35 UTC</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>2018-05-20 00:01:55 UTC</td>\n",
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       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-05-20 00:03:15 UTC</td>\n",
       "      <td>1.80</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-05-20 00:04:35 UTC</td>\n",
       "      <td>1.64</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-05-20 00:05:55 UTC</td>\n",
       "      <td>1.33</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  PM25cf1  TempF    RH\n",
       "0  2018-05-20 00:00:35 UTC     1.23   83.0  32.0\n",
       "1  2018-05-20 00:01:55 UTC     1.94   83.0  32.0\n",
       "2  2018-05-20 00:03:15 UTC     1.80   83.0  32.0\n",
       "3  2018-05-20 00:04:35 UTC     1.64   83.0  32.0\n",
       "4  2018-05-20 00:05:55 UTC     1.33   83.0  32.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def drop_and_rename_cols(df):\n",
    "    df = df[['created_at', 'PM2.5_CF1_ug/m3', 'Temperature_F', 'Humidity_%']]\n",
    "    df.columns = ['timestamp', 'PM25cf1', 'TempF', 'RH']\n",
    "    return df\n",
    "\n",
    "pa = (pa_full\n",
    "      .pipe(drop_and_rename_cols))\n",
    "pa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we check granularity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the Granularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order for the granularity of these measurements to match the AQS data,\n",
    "we want one average PM2.5 for each date (a 24-hour period).\n",
    "PurpleAir states that sensors take measurements every two minutes.\n",
    "Let's double-check the granularity of the raw measurements before we aggregate\n",
    "them to 24-hour periods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this we convert the column containing the date information from strings\n",
    "to `pd.TimeStamp` objects. The format of the date is different than the AQS format,\n",
    "which we describe as `'%Y-%m-%d %X %Z'`.\n",
    "As we soon see, `pandas` has special support for dataframes with an index of timestamps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM25cf1</th>\n",
       "      <th>TempF</th>\n",
       "      <th>RH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-20 00:00:35+00:00</th>\n",
       "      <td>1.23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-20 00:01:55+00:00</th>\n",
       "      <td>1.94</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           PM25cf1  TempF    RH\n",
       "timestamp                                      \n",
       "2018-05-20 00:00:35+00:00     1.23   83.0  32.0\n",
       "2018-05-20 00:01:55+00:00     1.94   83.0  32.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def parse_timestamps(df):\n",
    "    date_format = '%Y-%m-%d %X %Z'\n",
    "    times = pd.to_datetime(df['timestamp'], format=date_format)\n",
    "    return (df.assign(timestamp=times)\n",
    "            .set_index('timestamp'))\n",
    "\n",
    "pa = (pa_full\n",
    "      .pipe(drop_and_rename_cols)\n",
    "      .pipe(parse_timestamps))\n",
    "pa.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timestamps are tricky---notice that the original timestamps were given in the UTC\n",
    "time zone. However, the AQS data were averaged according to the *local time in\n",
    "California*, which is either seven or eight hours behind UTC time, depending on whether\n",
    "daylight saving time is in effect.\n",
    "This means we need to change the time zone of the PurpleAir timestamps to match\n",
    "the local time zone. The `df.tz_convert()` method operates on the index of the dataframe, which is one reason\n",
    "why we set the index of `pa` to the timestamps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM25cf1</th>\n",
       "      <th>TempF</th>\n",
       "      <th>RH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-19 17:00:35-07:00</th>\n",
       "      <td>1.23</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-19 17:01:55-07:00</th>\n",
       "      <td>1.94</td>\n",
       "      <td>83.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                           PM25cf1  TempF    RH\n",
       "timestamp                                      \n",
       "2018-05-19 17:00:35-07:00     1.23   83.0  32.0\n",
       "2018-05-19 17:01:55-07:00     1.94   83.0  32.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_tz(pa):\n",
    "    return pa.tz_convert('US/Pacific')\n",
    "\n",
    "pa = (pa_full\n",
    "      .pipe(drop_and_rename_cols)\n",
    "      .pipe(parse_timestamps)\n",
    "      .pipe(convert_tz))\n",
    "pa.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare the first two rows of this version of the dataframe to the previous one, we see that the time has changed to indicate the seven-hour difference from UTC. \n",
    "\n",
    "Visualizing timestamps can help us check the granularity of the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing timestamps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to visualize timestamps is to count how many appear in each 24-hour\n",
    "period, then plot those counts over time.\n",
    "To group time-series data in `pandas`, we can use the `df.resample()` method.\n",
    "This method works on dataframes that have an index of timestamps.\n",
    "It behaves like `df.groupby()`, except that we can specify how we want the\n",
    "timestamps to be grouped---we can group into dates, weeks, months, and many\n",
    "more options (the `D` argument tells resample to aggregate timestamps into individual dates):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_day = (pa.resample('D')\n",
    "           .size()\n",
    "           .rename('records_per_day')\n",
    "           .to_frame()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 293,  720, 1075, 1440, 2250])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percs = [10, 25, 50, 75, 100]\n",
    "np.percentile(per_day['records_per_day'], percs, method='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the number of measurements in a day varies widely. A line plot of these counts gives us a better sense of these variations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
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         "legendgroup": "",
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          "2018-05-22T00:00:00-07:00",
          "2018-05-23T00:00:00-07:00",
          "2018-05-24T00:00:00-07:00",
          "2018-05-25T00:00:00-07:00",
          "2018-05-26T00:00:00-07:00",
          "2018-05-27T00:00:00-07:00",
          "2018-05-28T00:00:00-07:00",
          "2018-05-29T00:00:00-07:00",
          "2018-05-30T00:00:00-07:00",
          "2018-05-31T00:00:00-07:00",
          "2018-06-01T00:00:00-07:00",
          "2018-06-02T00:00:00-07:00",
          "2018-06-03T00:00:00-07:00",
          "2018-06-04T00:00:00-07:00",
          "2018-06-05T00:00:00-07:00",
          "2018-06-06T00:00:00-07:00",
          "2018-06-07T00:00:00-07:00",
          "2018-06-08T00:00:00-07:00",
          "2018-06-09T00:00:00-07:00",
          "2018-06-10T00:00:00-07:00",
          "2018-06-11T00:00:00-07:00",
          "2018-06-12T00:00:00-07:00",
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          "2018-10-01T00:00:00-07:00",
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          "2018-10-10T00:00:00-07:00",
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          "2018-10-19T00:00:00-07:00",
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          "2018-10-21T00:00:00-07:00",
          "2018-10-22T00:00:00-07:00",
          "2018-10-23T00:00:00-07:00",
          "2018-10-24T00:00:00-07:00",
          "2018-10-25T00:00:00-07:00",
          "2018-10-26T00:00:00-07:00",
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          "2018-11-19T00:00:00-08:00",
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          "2018-11-21T00:00:00-08:00",
          "2018-11-22T00:00:00-08:00",
          "2018-11-23T00:00:00-08:00",
          "2018-11-24T00:00:00-08:00",
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          "2018-11-26T00:00:00-08:00",
          "2018-11-27T00:00:00-08:00",
          "2018-11-28T00:00:00-08:00",
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          "2019-02-25T00:00:00-08:00",
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          "2019-03-16T00:00:00-07:00",
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          "2019-03-27T00:00:00-07:00",
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          "2019-04-22T00:00:00-07:00",
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          "2019-05-10T00:00:00-07:00",
          "2019-05-11T00:00:00-07:00",
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          "2019-05-19T00:00:00-07:00",
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          "2019-05-22T00:00:00-07:00",
          "2019-05-23T00:00:00-07:00",
          "2019-05-24T00:00:00-07:00",
          "2019-05-25T00:00:00-07:00",
          "2019-05-26T00:00:00-07:00",
          "2019-05-27T00:00:00-07:00",
          "2019-05-28T00:00:00-07:00",
          "2019-05-29T00:00:00-07:00",
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          "2019-06-07T00:00:00-07:00",
          "2019-06-08T00:00:00-07:00",
          "2019-06-09T00:00:00-07:00",
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          "2019-06-16T00:00:00-07:00",
          "2019-06-17T00:00:00-07:00",
          "2019-06-18T00:00:00-07:00",
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          "2019-06-22T00:00:00-07:00",
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          "2019-06-25T00:00:00-07:00",
          "2019-06-26T00:00:00-07:00",
          "2019-06-27T00:00:00-07:00",
          "2019-06-28T00:00:00-07:00",
          "2019-06-29T00:00:00-07:00",
          "2019-06-30T00:00:00-07:00",
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          "2019-07-02T00:00:00-07:00",
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          "2019-07-05T00:00:00-07:00",
          "2019-07-06T00:00:00-07:00",
          "2019-07-07T00:00:00-07:00",
          "2019-07-08T00:00:00-07:00",
          "2019-07-09T00:00:00-07:00",
          "2019-07-10T00:00:00-07:00",
          "2019-07-11T00:00:00-07:00",
          "2019-07-12T00:00:00-07:00",
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          "2019-07-16T00:00:00-07:00",
          "2019-07-17T00:00:00-07:00",
          "2019-07-18T00:00:00-07:00",
          "2019-07-19T00:00:00-07:00",
          "2019-07-20T00:00:00-07:00",
          "2019-07-21T00:00:00-07:00",
          "2019-07-22T00:00:00-07:00",
          "2019-07-23T00:00:00-07:00",
          "2019-07-24T00:00:00-07:00",
          "2019-07-25T00:00:00-07:00",
          "2019-07-26T00:00:00-07:00",
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          "2019-07-28T00:00:00-07:00",
          "2019-07-29T00:00:00-07:00",
          "2019-07-30T00:00:00-07:00",
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          "2019-08-04T00:00:00-07:00",
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          "2019-08-06T00:00:00-07:00",
          "2019-08-07T00:00:00-07:00",
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          "2019-08-09T00:00:00-07:00",
          "2019-08-10T00:00:00-07:00",
          "2019-08-11T00:00:00-07:00",
          "2019-08-12T00:00:00-07:00",
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          "2019-08-14T00:00:00-07:00",
          "2019-08-15T00:00:00-07:00",
          "2019-08-16T00:00:00-07:00",
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          "2019-08-20T00:00:00-07:00",
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font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Date</text></g><g class=\"g-ytitle\" transform=\"translate(4.6591796875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.340625000000003,100.5)\" x=\"10.340625000000003\" y=\"100.5\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Records per day</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.line(per_day, x=per_day.index, y='records_per_day', \n",
    "        labels={'timestamp':'Date', 'records_per_day':'Records per day'},\n",
    "        width=550, height=250,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a fascinating plot. We see clear gaps in the data where there\n",
    "are no measurements. It appears that significant portions of data in July 2018\n",
    "and September 2019 are missing. Even when the sensor appears to be\n",
    "working, the number of measurements per day is slightly different. For\n",
    "instance, the plot is \"bumpy\" between August and October 2018, where dates have a\n",
    "varying number of measurements. We need to decide what we want to\n",
    "do with missing data. But perhaps more urgently: there are strange \"steps\" in the plot.\n",
    "Some dates have around 1,000 readings, some around 2,000, some around 700, and some around 1,400.\n",
    "If a sensor takes measurements every two minutes, there should be a maximum of 720 measurements\n",
    "per day. For a perfect sensor, the plot would display a flat line at 720\n",
    "measurements. This is clearly not the case. Let's investigate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the sampling rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deeper digging reveals that although PurpleAir sensors currently record data\n",
    "every 120 seconds, this was not always the case. Before May 30, 2019, sensors\n",
    "recorded data every 80 seconds, or 1,080 points a day.\n",
    "The change in sampling rate does explain the drop on May 30, 2019.\n",
    "Let's next look at the time periods where there were many more points than expected.\n",
    "This could mean that some measurements were duplicated in the data.\n",
    "We can check this by looking at the measurements for one day, say, January 1, 2019.\n",
    "We pass a string into `.loc` to filter timestamps for that date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2154"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pa.loc['2019-01-01'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are almost double the 1,080 expected readings.\n",
    "Let's check to see if readings are duplicated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01 13:52:30-08:00    2\n",
       "2019-01-01 12:02:21-08:00    2\n",
       "2019-01-01 11:49:01-08:00    2\n",
       "                            ..\n",
       "2019-01-01 21:34:10-08:00    2\n",
       "2019-01-01 11:03:41-08:00    2\n",
       "2019-01-01 04:05:38-08:00    2\n",
       "Name: timestamp, Length: 1077, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa.loc['2019-01-01'].index.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each timestamp appears exactly twice, and we can verify that all duplicated dates contain the same PM2.5 reading.\n",
    "Since this is also\n",
    "true for both temperature and humidity, we drop the duplicate rows from the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(502628, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def drop_duplicate_rows(df):\n",
    "    return df[~df.index.duplicated()]\n",
    "\n",
    "pa = (pa_full\n",
    "      .pipe(drop_and_rename_cols)\n",
    "      .pipe(parse_timestamps)\n",
    "      .pipe(convert_tz)\n",
    "      .pipe(drop_duplicate_rows))\n",
    "pa.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check, we remake the line plot of the number of records for a day, and this time we shade the regions where the counts are supposed to be contained:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_day = (pa.resample('D')\n",
    " .size().rename('records_per_day')\n",
    " .to_frame()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "config": {
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          "2018-06-11T00:00:00-07:00",
          "2018-06-12T00:00:00-07:00",
          "2018-06-13T00:00:00-07:00",
          "2018-06-14T00:00:00-07:00",
          "2018-06-15T00:00:00-07:00",
          "2018-06-16T00:00:00-07:00",
          "2018-06-17T00:00:00-07:00",
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          "2018-07-05T00:00:00-07:00",
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          "2018-07-07T00:00:00-07:00",
          "2018-07-08T00:00:00-07:00",
          "2018-07-09T00:00:00-07:00",
          "2018-07-10T00:00:00-07:00",
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          "2018-07-15T00:00:00-07:00",
          "2018-07-16T00:00:00-07:00",
          "2018-07-17T00:00:00-07:00",
          "2018-07-18T00:00:00-07:00",
          "2018-07-19T00:00:00-07:00",
          "2018-07-20T00:00:00-07:00",
          "2018-07-21T00:00:00-07:00",
          "2018-07-22T00:00:00-07:00",
          "2018-07-23T00:00:00-07:00",
          "2018-07-24T00:00:00-07:00",
          "2018-07-25T00:00:00-07:00",
          "2018-07-26T00:00:00-07:00",
          "2018-07-27T00:00:00-07:00",
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          "2018-08-04T00:00:00-07:00",
          "2018-08-05T00:00:00-07:00",
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          "2018-08-20T00:00:00-07:00",
          "2018-08-21T00:00:00-07:00",
          "2018-08-22T00:00:00-07:00",
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          "2018-08-26T00:00:00-07:00",
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          "2018-09-25T00:00:00-07:00",
          "2018-09-26T00:00:00-07:00",
          "2018-09-27T00:00:00-07:00",
          "2018-09-28T00:00:00-07:00",
          "2018-09-29T00:00:00-07:00",
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          "2018-10-01T00:00:00-07:00",
          "2018-10-02T00:00:00-07:00",
          "2018-10-03T00:00:00-07:00",
          "2018-10-04T00:00:00-07:00",
          "2018-10-05T00:00:00-07:00",
          "2018-10-06T00:00:00-07:00",
          "2018-10-07T00:00:00-07:00",
          "2018-10-08T00:00:00-07:00",
          "2018-10-09T00:00:00-07:00",
          "2018-10-10T00:00:00-07:00",
          "2018-10-11T00:00:00-07:00",
          "2018-10-12T00:00:00-07:00",
          "2018-10-13T00:00:00-07:00",
          "2018-10-14T00:00:00-07:00",
          "2018-10-15T00:00:00-07:00",
          "2018-10-16T00:00:00-07:00",
          "2018-10-17T00:00:00-07:00",
          "2018-10-18T00:00:00-07:00",
          "2018-10-19T00:00:00-07:00",
          "2018-10-20T00:00:00-07:00",
          "2018-10-21T00:00:00-07:00",
          "2018-10-22T00:00:00-07:00",
          "2018-10-23T00:00:00-07:00",
          "2018-10-24T00:00:00-07:00",
          "2018-10-25T00:00:00-07:00",
          "2018-10-26T00:00:00-07:00",
          "2018-10-27T00:00:00-07:00",
          "2018-10-28T00:00:00-07:00",
          "2018-10-29T00:00:00-07:00",
          "2018-10-30T00:00:00-07:00",
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          "2018-11-16T00:00:00-08:00",
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          "2018-11-21T00:00:00-08:00",
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          "2019-01-01T00:00:00-08:00",
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          "2019-02-24T00:00:00-08:00",
          "2019-02-25T00:00:00-08:00",
          "2019-02-26T00:00:00-08:00",
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          "2019-03-06T00:00:00-08:00",
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          "2019-03-08T00:00:00-08:00",
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          "2019-03-12T00:00:00-07:00",
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          "2019-03-16T00:00:00-07:00",
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          "2019-03-22T00:00:00-07:00",
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          "2019-03-25T00:00:00-07:00",
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          "2019-04-09T00:00:00-07:00",
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          "2019-04-12T00:00:00-07:00",
          "2019-04-13T00:00:00-07:00",
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          "2019-04-22T00:00:00-07:00",
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          "2019-04-24T00:00:00-07:00",
          "2019-04-25T00:00:00-07:00",
          "2019-04-26T00:00:00-07:00",
          "2019-04-27T00:00:00-07:00",
          "2019-04-28T00:00:00-07:00",
          "2019-04-29T00:00:00-07:00",
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          "2019-05-02T00:00:00-07:00",
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          "2019-05-04T00:00:00-07:00",
          "2019-05-05T00:00:00-07:00",
          "2019-05-06T00:00:00-07:00",
          "2019-05-07T00:00:00-07:00",
          "2019-05-08T00:00:00-07:00",
          "2019-05-09T00:00:00-07:00",
          "2019-05-10T00:00:00-07:00",
          "2019-05-11T00:00:00-07:00",
          "2019-05-12T00:00:00-07:00",
          "2019-05-13T00:00:00-07:00",
          "2019-05-14T00:00:00-07:00",
          "2019-05-15T00:00:00-07:00",
          "2019-05-16T00:00:00-07:00",
          "2019-05-17T00:00:00-07:00",
          "2019-05-18T00:00:00-07:00",
          "2019-05-19T00:00:00-07:00",
          "2019-05-20T00:00:00-07:00",
          "2019-05-21T00:00:00-07:00",
          "2019-05-22T00:00:00-07:00",
          "2019-05-23T00:00:00-07:00",
          "2019-05-24T00:00:00-07:00",
          "2019-05-25T00:00:00-07:00",
          "2019-05-26T00:00:00-07:00",
          "2019-05-27T00:00:00-07:00",
          "2019-05-28T00:00:00-07:00",
          "2019-05-29T00:00:00-07:00",
          "2019-05-30T00:00:00-07:00",
          "2019-05-31T00:00:00-07:00",
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          "2019-06-02T00:00:00-07:00",
          "2019-06-03T00:00:00-07:00",
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          "2019-06-05T00:00:00-07:00",
          "2019-06-06T00:00:00-07:00",
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          "2019-06-18T00:00:00-07:00",
          "2019-06-19T00:00:00-07:00",
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          "2019-07-04T00:00:00-07:00",
          "2019-07-05T00:00:00-07:00",
          "2019-07-06T00:00:00-07:00",
          "2019-07-07T00:00:00-07:00",
          "2019-07-08T00:00:00-07:00",
          "2019-07-09T00:00:00-07:00",
          "2019-07-10T00:00:00-07:00",
          "2019-07-11T00:00:00-07:00",
          "2019-07-12T00:00:00-07:00",
          "2019-07-13T00:00:00-07:00",
          "2019-07-14T00:00:00-07:00",
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          "2019-07-16T00:00:00-07:00",
          "2019-07-17T00:00:00-07:00",
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          "2019-07-19T00:00:00-07:00",
          "2019-07-20T00:00:00-07:00",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(per_day, x=per_day.index, y='records_per_day',\n",
    "              labels={'timestamp':'Date', 'records_per_day':'Records per day'}, \n",
    "              width=550, height=250)\n",
    "\n",
    "fig.add_annotation(x='2019-07-24', y=720,\n",
    "            text=\"720\", showarrow=False, yshift=10)\n",
    "fig.add_annotation(x='2019-07-24', y=1080,\n",
    "            text=\"1080\", showarrow=False, yshift=10)\n",
    "\n",
    "fig.add_hline(y=1080, line_width=3, line_dash=\"dot\", opacity=0.6)\n",
    "fig.add_hline(y=720, line_width=3, line_dash=\"dot\", opacity=0.6)\n",
    "fig.add_vline(x=\"2019-05-30\", line_width=3, line_dash=\"dash\", opacity=0.6)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After dropping duplicate dates, the plot of measurements per day\n",
    "looks much more consistent with the counts we expect.\n",
    "Careful readers will see two spikes above the maximum measurements around\n",
    "November of each year when daylight saving time is no longer in effect.\n",
    "When clocks are rolled back one hour, that day has 25 hours instead of the usual\n",
    "24 hours. Timestamps are tricky!\n",
    "\n",
    "But there are still missing measurements, and we need to decide what to do about them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plan is to create 24-hour averages of the measurements, but we don't want to use days when there are not enough measurements. We follow Barkjohn's analysis and only keep a 24-hour average if there are at least 90%\n",
    "of the possible points for that day. Remember that\n",
    "before May 30, 2019, there are 1,080 possible points in a day, and after that there are 720 possible points.\n",
    "We calculate the minimum number of measurements needed to keep per day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "needed_measurements_80s = 0.9 * 1080\n",
    "needed_measurements_120s = 0.9 * 720"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can determine which of the days have enough measurements to keep: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_date = pd.Timestamp('2019-05-30', tz='US/Pacific')\n",
    "\n",
    "def has_enough_readings(one_day):\n",
    "    [n] = one_day\n",
    "    date = one_day.name\n",
    "    return (n >= needed_measurements_80s\n",
    "            if date <= cutoff_date\n",
    "            else n >= needed_measurements_120s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2018-05-19 00:00:00-07:00    False\n",
       "2018-05-20 00:00:00-07:00     True\n",
       "2018-05-21 00:00:00-07:00     True\n",
       "2018-05-22 00:00:00-07:00     True\n",
       "2018-05-23 00:00:00-07:00     True\n",
       "Freq: D, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "should_keep = per_day.apply(has_enough_readings, axis='columns')\n",
    "should_keep.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to average together the readings for each day and then remove the days without enough readings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PM25cf1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-20 00:00:00-07:00</th>\n",
       "      <td>2.48</td>\n",
       "      <td>83.35</td>\n",
       "      <td>28.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-21 00:00:00-07:00</th>\n",
       "      <td>3.00</td>\n",
       "      <td>83.25</td>\n",
       "      <td>29.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           PM25cf1  TempF     RH\n",
       "timestamp                                       \n",
       "2018-05-20 00:00:00-07:00     2.48  83.35  28.72\n",
       "2018-05-21 00:00:00-07:00     3.00  83.25  29.91"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_daily_avgs(pa):\n",
    "    should_keep = (pa.resample('D')\n",
    "                   ['PM25cf1']\n",
    "                   .size()\n",
    "                   .to_frame()\n",
    "                   .apply(has_enough_readings, axis='columns'))\n",
    "    return (pa.resample('D')\n",
    "            .mean()\n",
    "            .loc[should_keep])\n",
    "\n",
    "pa = (pa_full\n",
    "      .pipe(drop_and_rename_cols)\n",
    "      .pipe(parse_timestamps)\n",
    "      .pipe(convert_tz)\n",
    "      .pipe(drop_duplicate_rows)\n",
    "      .pipe(compute_daily_avgs))\n",
    "pa.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the average daily PM2.5 readings for instrument A, and we need to repeat on instrument B the data wrangling we just performed on\n",
    "instrument A. Fortunately, we can reuse the same pipeline. For brevity, we don't include that wrangling here.\n",
    "But we need to decide what to do if the PM2.5 averages differ. Barkjohn dropped rows if the PM2.5 values for A and B differed by more than 61%, or by more than 5 µg m⁻³. For this pair of sensors, that leads to dropping 12 of the 500+ rows. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, it takes a lot of work to prepare and clean these data: we handled \n",
    "missing data, aggregated the readings for each instrument, averaged the readings\n",
    "together from the two instruments, and removed rows where they disagreed.\n",
    "This work has given us a set of PM2.5 readings that we are more confident in.\n",
    "We know that each PM2.5 value in the final dataframe is the daily average from\n",
    "two separate instruments that generated consistent and complete readings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fully replicate Barkjohn's analysis, we would need to repeat this process over all the PurpleAir sensors.\n",
    "Then we would repeat the AQS cleaning procedure on all the AQS sensors.\n",
    "Finally, we would merge the PurpleAir and AQS data together.\n",
    "This procedure produces daily average readings for each collocated sensor pair.\n",
    "For brevity, we omit this code.\n",
    "Instead, we proceed with the final steps of the analysis using the group's dataset.\n",
    "We begin with an EDA with an eye toward modeling. "
   ]
  }
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